STUN: forward-time simulation on TUnable fitNess landscapes in recombining populations.

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STUN is a flexible and user-friendly software package for simulating the polygenic adaptation of recombining haploid populations using either new mutations or standing genetic variation. STUN assumes that populations adapt to sudden environmental changes by undergoing selection on a new fitness landscape. With pre-implemented fitness landscape models like Rough Mount Fuji, NK, Block, additive, and House-of-Cards, users can explore the effect of different levels of epistasis (ruggedness of the fitness landscape). Custom fitness landscapes and recombination maps can also be defined. STUN empowers both experimentalists and advanced programmers to study the evolution of complex polygenic traits and to dissect the adaptation process.\nAvailability and implementation: STUN is implemented in Rust. Its source code is available at https://github.com/banklab/STUN and archived on Zenodo under doi: 10.5281/zenodo.10246377. The repository includes a link to the software's manual and binary files for Linux, macOS and Windows.", "claims": [{"id": "claim-method", "statement": "To assess this relationship, we calculated the mean real time (wall clock time) for each simulation run, where 10 replicate populations sequentially adapted on a single fitness landscape using either SGV or new mutations (Fig. 2).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38075480", "locator": "sections[9] \"4 Performance\" paragraph 2", "quote": "To assess this relationship, we calculated the mean real time (wall clock time) for each simulation run, where 10 replicate populations sequentially adapted on a single fitness landscape using either SGV or new mutations (Fig. 2)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "There are no new data associated with this article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38075480", "locator": "sections[19] \"Data availability\" paragraph 2", "quote": "There are no new data associated with this article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Remarkably, even with large sizes, such as a fitness landscape with 10 loci and a population of 104 individuals, the computation time for 10 simulations remains relatively short.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38075480", "locator": "sections[9] \"4 Performance\" paragraph 2", "quote": "Remarkably, even with large sizes, such as a fitness landscape with 10 loci and a population of 104 individuals, the computation time for 10 simulations remains relatively short."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Remarkably, even with large sizes, such as a fitness landscape with 10 loci and a population of 104 individuals, the computation time for 10 simulations remains relatively short.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38075480"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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